New trends on moving object detection in video images captured by a moving camera: A survey

被引:143
作者
Yazdi, Mehran [1 ,2 ]
Bouwmans, Thierry [2 ]
机构
[1] Shiraz Univ, Lab Signal & Image Proc, Fac Elect & Comp Engn, Shiraz, Iran
[2] Univ La Rochelle, Lab MIA, La Rochelle, France
关键词
Moving object detection; Moving camera; Background subtraction; Motion compensation; REAL-TIME TRACKING; BACKGROUND SUBTRACTION; VISUAL SURVEILLANCE; MOTION DETECTION; PERFORMANCE EVALUATION; MULTITARGET TRACKING; APPEARANCE MODELS; SHADOW DETECTION; PARTICLE FILTER; ROBUST;
D O I
10.1016/j.cosrev.2018.03.001
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a survey on the latest methods of moving object detection in video sequences captured by a moving camera. Although many researches and excellent works have reviewed the methods of object detection and background subtraction for a fixed camera, there is no survey which presents a complete review of the existing different methods in the case of moving camera. Most methods in this field can be classified into four categories; modeling based background subtraction, trajectory classification, low rank and sparse matrix decomposition, and object tracking. We discuss in details each category and present the main methods which proposed improvements in the general concept of the techniques. Wealso present challenges and main concerns in this field as well as performance metrics and some benchmark databases available to evaluate the performance of different moving object detection algorithms. (C) 2018 Elsevier Inc. All rights reserved.
引用
收藏
页码:157 / 177
页数:21
相关论文
共 208 条
[41]  
[Anonymous], INT J ENG RES TECHNO
[42]  
[Anonymous], 2016, IEEE T PATTERN ANAL, DOI DOI 10.1109/TPAMI.2015.2509974
[43]  
[Anonymous], J ELECT INF TECHNOL
[44]  
[Anonymous], 2003, Multiple view geometry in computer vision
[45]   Dynamic texture representation using a deep multi-scale convolutional network [J].
Arashloo, Shervin Rahimzadeh ;
Amirani, Mehdi Chehel ;
Noroozi, Ardeshir .
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2017, 43 :89-97
[46]   A keypoint-based method for background modeling and foreground detection using a PTZ camera [J].
Avola, Danilo ;
Cinque, Luigi ;
Foresti, Gian Luca ;
Massaroni, Cristiano ;
Pannone, Daniele .
PATTERN RECOGNITION LETTERS, 2017, 96 :96-105
[47]  
Babaee M., 2017, PATTERN RECOGN
[48]   Robust Object Tracking with Online Multiple Instance Learning [J].
Babenko, Boris ;
Yang, Ming-Hsuan ;
Belongie, Serge .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2011, 33 (08) :1619-1632
[49]  
Bagherzadeh MA, 2014, RSI INT CONF ROBOT M, P823, DOI 10.1109/ICRoM.2014.6991006
[50]  
Balan A.O., 2006, COMPUTER VISION PATT, V1, P758